OBJECTIVE To explore the characteristics and influencing factors of immune-related adverse events （irAEs） induced by tislelizumab in patients with liver cancer. METHODS A retrospective cohort of 203 liver cancer patients treated with tislelizumab in Guangxi Medical University Cancer Hospital from May 2022 to March 2024 was included. These patients were divided into an irAEs group （58 cases） and a non-irAEs group （145 cases）. Clinical data were collected and compared between the two groups. A multivariate logistic regression model was employed to analyze factors influencing the occurrence of irAEs and establish a predictive model. The receiver operator characteristic （ROC） curve was plotted to evaluate the predictive value of the model for the occurrence of irAEs. The correlation between irAEs and overall survival （OS） as well as progression free survival （PFS） in patients was analyzed using the Kaplan-Meier method. RESULTS The irAEs induced by tislelizumab in liver cancer patients were predominantly grade 1-2 （89.71%）， mainly manifesting as hematological toxicity （42.65%） and hepatotoxicity （20.59%）， and mostly occurred within 1-12 cycles after tislelizumab treatment. Compared with liver cancer patients without underlying liver diseases， those with chronic hepatitis B had a higher incidence of irAEs. Statistically significant differences were observed between the irAEs and non-irAEs groups in terms of the number of patients with a China Liver Cancer Staging （CNLC） stage ≥Ⅱ， white blood cell count， neutrophil count， systemic immune-inflammation index （SII）， and neutrophil-to-lymphocyte ratio （NLR） （P＜0.05）. Multivariate Logistic regression analysis revealed that CNLC stage ≥Ⅱ was an independent risk factor for the occurrence of irAEs （P＝0.027）. The ROC curve indicated that neutrophil count， white blood cell count， NLR， and SII all demonstrated certain predictive potential for the occurrence of irAEs （with area under the curve values of 0.614， 0.592，0.591， and 0.589， respectively）. The Kaplan-Meier survival curve showed no statistically significant differences in PFS and OS between the irAEs and non-irAEs groups， among patients with different grades of irAEs， and among irAEs patients with different CNLC stages （P＞0.05）. CONCLUSION The irAEs induced by tislelizumab in liver cancer patients are relatively mild （grade 1-2），mainly manifesting as hematological toxicity and hepatotoxicity. Liver cancer patients with concurrent chronic hepatitis B are at a higher risk of developing irAEs. CNLC stage ≥Ⅱ is an independent risk factor for irAEs induced by tislelizumab. Neutrophil count， white blood cell count， NLR， and SII have certain predictive value for the occurrence of irAEs.

BACKGROUND/AIMS: To investigate the role of selected serum microRNA (miRNA) levels as potential noninvasive biomarkers for differentiating S0–S2 (early fibrosis) from S3–S4 (late fibrosis) in patients with a chronic hepatitis B virus (HBV) infection. METHODS: One hundred twenty-three treatment-naive patients with a chronic HBV infection who underwent a liver biopsy were enrolled in this study. The levels of selected miRNAs were measured using a real-time quantitative polymerase chain reaction assay. A logistic regression analysis was performed to assess factors associated with fibrosis progression. Receiver operating characteristic (ROC) curve and discriminant analyses validated these the ability of these predicted variables to discriminate S0–S2 from S3–S4. RESULTS: Serum miR-29, miR-143, miR-223, miR-21, and miR-374 levels were significantly downregulated as fibrosis progressed from S0–S2 to S3–S4 (p < 0.05), but not miR-16. The multivariate logistic regression analysis identified a panel of three miRNAs and platelets that were associated with a high diagnostic accuracy in discriminating S0–S2 from S3–S4, with an area under the curve of 0.936. CONCLUSIONS: The levels of the studied miRNAs, with the exception of miR-16, varied with fibrosis progression. A panel was identified that was capable of discriminating S0–S2 from S3–S4, indicating that serum miRNA levels could serve as a potential noninvasive biomarker of fibrosis progression.
Biomarkers ; Biopsy ; Early Diagnosis* ; Fibrosis ; Hepatitis B virus ; Hepatitis B* ; Hepatitis B, Chronic ; Hepatitis* ; Humans ; Liver Cirrhosis* ; Liver* ; Logistic Models ; MicroRNAs* ; Polymerase Chain Reaction ; ROC Curve